Wearable device data enable new perspectives on global mobility beyond average step counts, highlighting variability as a critical marker of health inequality, especially in aging populations. As average ages increase worldwide, such data provide opportunities to understand how activity levels vary across societies and how these variabilities affect healthy aging. This study investigates how demographic, economic, and environmental parameters shape physical activity variability across 34 countries. We integrate step inequality, gender gaps, urbanization, and WHO aging metrics, and apply clustering algorithms, principal component analysis, and graph-based community detection to reveal hidden structures. Multivariate statistics and network-based analysis identify structural patterns and clusters of countries with shared characteristics. The findings highlight how demographic aging and urban environments shape variability in activity and related health outcomes. Results demonstrate the value of combining clustering with network science to uncover hidden structures in population health, offering actionable insights for aging research, public health, and urban planning.